Overview

Dataset statistics

Number of variables11
Number of observations6829
Missing cells9942
Missing cells (%)13.2%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory587.0 KiB
Average record size in memory88.0 B

Variable types

NUM8
CAT2
BOOL1

Warnings

Dataset has 1 (< 0.1%) duplicate rows Duplicates
Player has a high cardinality: 6809 distinct values High cardinality
Forty has 172 (2.5%) missing values Missing
Vertical has 1568 (23.0%) missing values Missing
BenchReps has 2198 (32.2%) missing values Missing
BroadJump has 1622 (23.8%) missing values Missing
Cone has 2226 (32.6%) missing values Missing
Shuttle has 2156 (31.6%) missing values Missing
Player is uniformly distributed Uniform

Reproduction

Analysis started2021-01-05 10:33:09.226032
Analysis finished2021-01-05 10:33:26.020518
Duration16.79 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Player
Categorical

HIGH CARDINALITY
UNIFORM

Distinct6809
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size53.4 KiB
Kyle Murphy
 
2
Mike Bell
 
2
Brandon Jones
 
2
Josh Allen
 
2
Stanford Samuels
 
2
Other values (6804)
6819 
ValueCountFrequency (%) 
Kyle Murphy2< 0.1%
 
Mike Bell2< 0.1%
 
Brandon Jones2< 0.1%
 
Josh Allen2< 0.1%
 
Stanford Samuels2< 0.1%
 
Nate Davis2< 0.1%
 
Zach Allen2< 0.1%
 
Daniel Thomas2< 0.1%
 
Josh Jones2< 0.1%
 
Nick Harris2< 0.1%
 
Other values (6799)680999.7%
 
2021-01-05T18:33:26.134342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6789 ?
Unique (%)99.4%
2021-01-05T18:33:26.285935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length13
Mean length12.87787377
Min length7

Pos
Categorical

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size53.4 KiB
WR
942 
CB
694 
RB
597 
DE
500 
DT
480 
Other values (22)
3616 
ValueCountFrequency (%) 
WR94213.8%
 
CB69410.2%
 
RB5978.7%
 
DE5007.3%
 
DT4807.0%
 
OT4686.9%
 
OLB4246.2%
 
QB3845.6%
 
TE3785.5%
 
OG3775.5%
 
Other values (17)158523.2%
 
2021-01-05T18:33:26.660932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-05T18:33:26.773703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.040415873
Min length1

Ht
Real number (ℝ≥0)

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.81461415
Minimum65
Maximum82
Zeros0
Zeros (%)0.0%
Memory size53.4 KiB
2021-01-05T18:33:26.878423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile69
Q172
median74
Q376
95-th percentile78
Maximum82
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.616508961
Coefficient of variation (CV)0.03544703161
Kurtosis-0.4595497277
Mean73.81461415
Median Absolute Deviation (MAD)2
Skewness-0.1539230461
Sum504080
Variance6.846119143
MonotocityNot monotonic
2021-01-05T18:33:26.976231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
7597214.2%
 
7489913.2%
 
7685912.6%
 
7383912.3%
 
7277711.4%
 
776209.1%
 
716038.8%
 
704316.3%
 
782994.4%
 
692393.5%
 
Other values (8)2914.3%
 
ValueCountFrequency (%) 
651< 0.1%
 
66100.1%
 
67330.5%
 
68781.1%
 
692393.5%
 
ValueCountFrequency (%) 
822< 0.1%
 
8140.1%
 
80380.6%
 
791251.8%
 
782994.4%
 

Wt
Real number (ℝ≥0)

Distinct206
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.9799385
Minimum149
Maximum375
Zeros0
Zeros (%)0.0%
Memory size53.4 KiB
2021-01-05T18:33:27.111870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum149
5-th percentile186
Q1206
median233
Q3279
95-th percentile320
Maximum375
Range226
Interquartile range (IQR)73

Descriptive statistics

Standard deviation44.9607657
Coefficient of variation (CV)0.1850390036
Kurtosis-0.9096455909
Mean242.9799385
Median Absolute Deviation (MAD)31
Skewness0.5230421595
Sum1659310
Variance2021.470453
MonotocityNot monotonic
2021-01-05T18:33:27.243518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
211931.4%
 
205891.3%
 
210871.3%
 
204861.3%
 
195831.2%
 
213831.2%
 
209811.2%
 
193811.2%
 
197801.2%
 
202791.2%
 
Other values (196)598787.7%
 
ValueCountFrequency (%) 
1491< 0.1%
 
1551< 0.1%
 
1561< 0.1%
 
1602< 0.1%
 
1631< 0.1%
 
ValueCountFrequency (%) 
3751< 0.1%
 
3701< 0.1%
 
3692< 0.1%
 
3661< 0.1%
 
3641< 0.1%
 

Forty
Real number (ℝ≥0)

MISSING

Distinct160
Distinct (%)2.4%
Missing172
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean4.882804567
Minimum4.22
Maximum9.99
Zeros0
Zeros (%)0.0%
Memory size53.4 KiB
2021-01-05T18:33:27.381151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.22
5-th percentile4.41
Q14.54
median4.71
Q35
95-th percentile5.42
Maximum9.99
Range5.77
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation0.7860884918
Coefficient of variation (CV)0.1609911847
Kurtosis32.41987587
Mean4.882804567
Median Absolute Deviation (MAD)0.2
Skewness5.424068315
Sum32504.83
Variance0.617935117
MonotocityNot monotonic
2021-01-05T18:33:27.503361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.51492.2%
 
4.561362.0%
 
4.651352.0%
 
9.991321.9%
 
4.621321.9%
 
4.581241.8%
 
4.521221.8%
 
4.591221.8%
 
4.531211.8%
 
4.61121.6%
 
Other values (150)537278.7%
 
(Missing)1722.5%
 
ValueCountFrequency (%) 
4.222< 0.1%
 
4.241< 0.1%
 
4.261< 0.1%
 
4.273< 0.1%
 
4.2850.1%
 
ValueCountFrequency (%) 
9.991321.9%
 
6.051< 0.1%
 
61< 0.1%
 
5.991< 0.1%
 
5.861< 0.1%
 

Vertical
Real number (ℝ≥0)

MISSING

Distinct56
Distinct (%)1.1%
Missing1568
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean32.88718875
Minimum17.5
Maximum46
Zeros0
Zeros (%)0.0%
Memory size53.4 KiB
2021-01-05T18:33:27.635104image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum17.5
5-th percentile25.5
Q130
median33
Q336
95-th percentile39.5
Maximum46
Range28.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.200382395
Coefficient of variation (CV)0.1277209319
Kurtosis-0.1671683509
Mean32.88718875
Median Absolute Deviation (MAD)3
Skewness-0.2014995711
Sum173019.5
Variance17.64321227
MonotocityNot monotonic
2021-01-05T18:33:27.751538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
332754.0%
 
342603.8%
 
33.52563.7%
 
35.52473.6%
 
352393.5%
 
34.52373.5%
 
362333.4%
 
32.52333.4%
 
322203.2%
 
311942.8%
 
Other values (46)286742.0%
 
(Missing)156823.0%
 
ValueCountFrequency (%) 
17.51< 0.1%
 
191< 0.1%
 
19.53< 0.1%
 
201< 0.1%
 
20.570.1%
 
ValueCountFrequency (%) 
461< 0.1%
 
45.51< 0.1%
 
4540.1%
 
44.52< 0.1%
 
443< 0.1%
 

BenchReps
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)1.0%
Missing2198
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean20.83092205
Minimum2
Maximum49
Zeros0
Zeros (%)0.0%
Memory size53.4 KiB
2021-01-05T18:33:27.876237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q116
median21
Q325
95-th percentile32
Maximum49
Range47
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.360797387
Coefficient of variation (CV)0.3053536168
Kurtosis0.09056084225
Mean20.83092205
Median Absolute Deviation (MAD)4
Skewness0.2611579457
Sum96468
Variance40.4597434
MonotocityNot monotonic
2021-01-05T18:33:28.017859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
192894.2%
 
212824.1%
 
232794.1%
 
202673.9%
 
242653.9%
 
222653.9%
 
172563.7%
 
182533.7%
 
152433.6%
 
252213.2%
 
Other values (35)201129.4%
 
(Missing)219832.2%
 
ValueCountFrequency (%) 
21< 0.1%
 
32< 0.1%
 
450.1%
 
550.1%
 
6100.1%
 
ValueCountFrequency (%) 
491< 0.1%
 
453< 0.1%
 
4440.1%
 
431< 0.1%
 
4240.1%
 

BroadJump
Real number (ℝ≥0)

MISSING

Distinct62
Distinct (%)1.2%
Missing1622
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean114.3312848
Minimum74
Maximum147
Zeros0
Zeros (%)0.0%
Memory size53.4 KiB
2021-01-05T18:33:28.153526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile97
Q1109
median115
Q3121
95-th percentile128
Maximum147
Range73
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.319730821
Coefficient of variation (CV)0.08151514117
Kurtosis9.770462802e-05
Mean114.3312848
Median Absolute Deviation (MAD)6
Skewness-0.4152172561
Sum595323
Variance86.85738258
MonotocityNot monotonic
2021-01-05T18:33:28.305725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1202784.1%
 
1182523.7%
 
1172353.4%
 
1162343.4%
 
1152343.4%
 
1212323.4%
 
1192103.1%
 
1132103.1%
 
1141972.9%
 
1121912.8%
 
Other values (52)293443.0%
 
(Missing)162223.8%
 
ValueCountFrequency (%) 
741< 0.1%
 
781< 0.1%
 
822< 0.1%
 
841< 0.1%
 
8540.1%
 
ValueCountFrequency (%) 
1471< 0.1%
 
1413< 0.1%
 
1401< 0.1%
 
1393< 0.1%
 
1383< 0.1%
 

Cone
Real number (ℝ≥0)

MISSING

Distinct294
Distinct (%)6.4%
Missing2226
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean7.351562025
Minimum3.97
Maximum9.99
Zeros0
Zeros (%)0.0%
Memory size53.4 KiB
2021-01-05T18:33:28.447385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.97
5-th percentile6.63
Q16.96
median7.2
Q37.63
95-th percentile9.99
Maximum9.99
Range6.02
Interquartile range (IQR)0.67

Descriptive statistics

Standard deviation0.9622254
Coefficient of variation (CV)0.1308872042
Kurtosis4.116245971
Mean7.351562025
Median Absolute Deviation (MAD)0.3
Skewness0.244573942
Sum33839.24
Variance0.9258777203
MonotocityNot monotonic
2021-01-05T18:33:28.573084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.992924.3%
 
7.09761.1%
 
7.07751.1%
 
7671.0%
 
7.08661.0%
 
6.9630.9%
 
7.12590.9%
 
7.15580.8%
 
6.94540.8%
 
6.96540.8%
 
Other values (284)373954.8%
 
(Missing)222632.6%
 
ValueCountFrequency (%) 
3.971< 0.1%
 
3.991< 0.1%
 
4.011< 0.1%
 
4.032< 0.1%
 
4.041< 0.1%
 
ValueCountFrequency (%) 
9.992924.3%
 
9.121< 0.1%
 
9.041< 0.1%
 
91< 0.1%
 
8.841< 0.1%
 

Shuttle
Real number (ℝ≥0)

MISSING

Distinct236
Distinct (%)5.1%
Missing2156
Missing (%)31.6%
Infinite0
Infinite (%)0.0%
Mean4.839659747
Minimum3.73
Maximum9.99
Zeros0
Zeros (%)0.0%
Memory size53.4 KiB
2021-01-05T18:33:28.706762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.73
5-th percentile4.04
Q14.21
median4.39
Q34.67
95-th percentile9.99
Maximum9.99
Range6.26
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation1.451087154
Coefficient of variation (CV)0.2998324738
Kurtosis7.270869814
Mean4.839659747
Median Absolute Deviation (MAD)0.21
Skewness2.914848806
Sum22615.73
Variance2.10565393
MonotocityNot monotonic
2021-01-05T18:33:28.838401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.992934.3%
 
4.41051.5%
 
4.281011.5%
 
4.21941.4%
 
4.2921.3%
 
4.15841.2%
 
4.25841.2%
 
4.18821.2%
 
4.32811.2%
 
4.07771.1%
 
Other values (226)358052.4%
 
(Missing)215631.6%
 
ValueCountFrequency (%) 
3.731< 0.1%
 
3.751< 0.1%
 
3.781< 0.1%
 
3.81< 0.1%
 
3.812< 0.1%
 
ValueCountFrequency (%) 
9.992934.3%
 
8.281< 0.1%
 
8.151< 0.1%
 
8.131< 0.1%
 
8.061< 0.1%
 

pro bowl?
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.4 KiB
0
6249 
1
 
580
ValueCountFrequency (%) 
0624991.5%
 
15808.5%
 
2021-01-05T18:33:28.937184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2021-01-05T18:33:16.016227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:16.170943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:16.316610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:16.440285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:16.566910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:16.694074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:16.821747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:16.945415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:17.071147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:17.214629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:17.370245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:17.500862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:17.637496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:17.767166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:17.893811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:18.033978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:18.162139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:18.292789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:18.600512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:18.745124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:18.876771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:19.011411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:19.155027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:19.285678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:19.433283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:19.574847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:19.721994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:19.861620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:19.994266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:20.123920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:20.268532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:20.404169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:20.544793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:20.678436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:20.816067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:20.940752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:21.066449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:21.195071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:21.326719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:21.456372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:21.585028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:21.725652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:21.871263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:22.019867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:22.169497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:22.310103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:22.463717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:22.613364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:22.765947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:22.911531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:23.078086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:23.235699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:23.376290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:23.507968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:23.656097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:23.790703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:23.953269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:24.101870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:24.255459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:24.404061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:24.549673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:24.696875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:24.830549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:24.963163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-05T18:33:29.013983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-05T18:33:29.238371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-05T18:33:29.427077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-05T18:33:29.623536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-01-05T18:33:25.202073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:25.505472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:25.731447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T18:33:25.899804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

PlayerPosHtWtFortyVerticalBenchRepsBroadJumpConeShuttlepro bowl?
0John AbrahamOLB762524.55NaNNaNNaNNaNNaN1
1Shaun AlexanderRB722184.58NaNNaNNaNNaNNaN1
2Darnell AlfordOT763345.5625.023.094.08.484.980
3Kyle AllamonTE742534.9729.0NaN104.07.294.490
4Rashard AndersonCB742064.5534.0NaN123.07.184.150
5Jake AriansK70202NaNNaNNaNNaNNaNNaN0
6LaVar ArringtonOLB752504.53NaNNaNNaNNaNNaN1
7Corey AtkinsOLB722374.7231.021.0112.07.964.390
8Kyle AtteberryK72167NaNNaNNaNNaNNaNNaN0
9Reggie AustinCB691754.4435.017.0119.07.034.140

Last rows

PlayerPosHtWtFortyVerticalBenchRepsBroadJumpConeShuttlepro bowl?
6819Rob WindsorDL762854.9028.521.0111.07.474.440
6820Antoine WinfieldS702054.4536.0NaN124.09.999.991
6821Tristan WirfsOL773224.8536.524.0121.07.654.680
6822Steven WirtelLS762274.7626.0NaN120.07.124.280
6823Charlie WoernerTE772454.7834.521.0120.07.184.460
6824D.J. WonnumDL772544.7334.520.0123.07.254.440
6825Dom Wood-AndersonTE762574.9235.0NaN119.09.999.990
6826David WoodwardLB742354.7933.516.0114.07.344.370
6827Chase YoungDL772659.99NaNNaNNaN9.999.991
6828Jabari ZunigaDL752534.6433.029.0127.09.999.990

Duplicate rows

Most frequent

PlayerPosHtWtFortyVerticalBenchRepsBroadJumpConeShuttlepro bowl?count
0Yetur Gross-MatosDL772649.9934.020.0120.09.999.9902